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      "('iteration', 259300, ':cross-entropy-loss', array(203.71163940429688, dtype=float32))\n",
      "('iteration', 259400, ':cross-entropy-loss', array(186.57009887695312, dtype=float32))\n",
      "('iteration', 259500, ':cross-entropy-loss', array(239.9289093017578, dtype=float32))\n",
      "('iteration', 259600, ':cross-entropy-loss', array(187.18011474609375, dtype=float32))\n",
      "('iteration', 259700, ':cross-entropy-loss', array(218.22610473632812, dtype=float32))\n",
      "('iteration', 259800, ':cross-entropy-loss', array(234.43515014648438, dtype=float32))\n",
      "('iteration', 259900, ':cross-entropy-loss', array(177.401611328125, dtype=float32))\n",
      "('iteration', 260000, ':cross-entropy-loss', array(175.61572265625, dtype=float32))\n",
      "('iteration', 260100, ':cross-entropy-loss', array(183.09291076660156, dtype=float32))\n",
      "('iteration', 260200, ':cross-entropy-loss', array(178.52069091796875, dtype=float32))\n",
      "('iteration', 260300, ':cross-entropy-loss', array(170.22885131835938, dtype=float32))\n",
      "('iteration', 260400, ':cross-entropy-loss', array(186.12586975097656, dtype=float32))\n",
      "('iteration', 260500, ':cross-entropy-loss', array(174.24270629882812, dtype=float32))\n",
      "('iteration', 260600, ':cross-entropy-loss', array(177.4122314453125, dtype=float32))\n",
      "('iteration', 260700, ':cross-entropy-loss', array(218.74209594726562, dtype=float32))\n",
      "('iteration', 260800, ':cross-entropy-loss', array(210.3205108642578, dtype=float32))\n",
      "('iteration', 260900, ':cross-entropy-loss', array(182.2120361328125, dtype=float32))\n",
      "('iteration', 261000, ':cross-entropy-loss', array(160.5540008544922, dtype=float32))\n",
      "('iteration', 261100, ':cross-entropy-loss', array(161.58139038085938, dtype=float32))\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-893ce7d626b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mniter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0msolver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;31m#print solver.net.blobs.keys()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;31m#     train_loss[i] = solver.net.blobs['l2_error'].data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.insert(0,'/home/csunix/schtmt/NewFolder/caffe_Sep/python')\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import caffe\n",
    "#% matplotlib inline#THIS LINE HAS ERROR -> USE THE FOLLOWING 2 LINES\n",
    "# from IPython import get_ipython\n",
    "# get_ipython().run_line_magic('matplotlib', 'inline')\n",
    "\n",
    "caffe.set_device(0)\n",
    "caffe.set_mode_gpu()\n",
    "solver = caffe.SGDSolver('solver_patch.prototxt')\n",
    "niter = 600000\n",
    "train_loss = np.zeros(niter)\n",
    "\n",
    "for i in range(niter):\n",
    "    solver.step(1)\n",
    "    #print solver.net.blobs.keys()\n",
    "    #     train_loss[i] = solver.net.blobs['l2_error'].data\n",
    "    loss = solver.net.blobs['cross_entropy_loss'].data\n",
    "    if (i%100 == 0):\n",
    "        print('iteration', i, ':cross-entropy-loss', loss) \n",
    "    train_loss[i] = loss\n",
    "    np.save('conv_lstm_AE_train_loss_22Sep',train_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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